Generative AI tools like ChatGPT show tremendous potential to revolutionize K-12 education through personalized learning and innovative teaching methods, but successful implementation requires comprehensive teacher training, ethical guidelines, and systematic integration frameworks to address current gaps in research and practice.
Objective: This systematic review aimed to provide a comprehensive overview of the use of Generative Artificial Intelligence (GAI) in K-12 education, specifically examining how AI and GAI have been used to support, enhance, and innovate teaching and learning processes. The study sought to identify opportunities and challenges in K-12 educational contexts, explore pedagogical and technical challenges teachers face when integrating GAI, and identify current gaps in scientific literature regarding GAI use in primary and secondary education.
Methods: The research employed a systematic literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. The study examined articles published between 2016 and 2024, selecting 197 relevant studies from five databases (Scopus, ERIC, WOS) and two specialized journals (British Educational Research Journal and Springer Journals). The methodology included the use of a Mentefacto Map to identify keywords and define inclusion and exclusion criteria, ensuring a systematic and replicable approach. The PICOC framework (Population—Intervention—Comparison—Outcome—Context) was used to structure research questions, with the target population being K-12 students, intervention involving GAI integration with complementary technologies, and outcomes focusing on improved student understanding, engagement, collaboration, creativity, and inclusion. The research also employed backward snowballing techniques to identify additional relevant publications, ultimately analyzing 130 highly relevant studies and 67 semi-relevant studies.
Key Findings: The review revealed several significant findings about GAI implementation in K-12 education. First, GAI offers substantial opportunities to personalize learning experiences, motivate students, improve assessment methods, and introduce innovative teaching practices, with ChatGPT serving as a prominent example of successful educational application. However, the study identified critical challenges including the urgent need for continuous teacher training on ICT, development of ministerial guidelines addressing ethical and privacy concerns, and creation of structured frameworks for AI implementation. The research showed a notable lack of concrete studies and specific experiments extending beyond STEM disciplines to include arts and humanities. Additionally, there was insufficient practical guidance for educators on selecting appropriate educational technologies for their teaching objectives. The geographical distribution revealed that 30% of studies originated from Europe, 28% from both the United States and Asia, with limited representation from other regions. Publication trends showed dramatic growth, with 54% of articles published in 2023, indicating rapidly increasing research interest following ChatGPT's release in late 2022.
Implications: The findings contribute significantly to the field of AI in education by establishing that GAI can transform educational systems while highlighting the complexity of implementation challenges. The study emphasizes that successful GAI integration requires more than technological adoption—it demands comprehensive pedagogical transformation, teacher professional development, and institutional support. The research provides evidence that GAI can facilitate personalized learning, improve student engagement, and support innovative teaching methodologies, but only when implemented with proper training and ethical considerations. The study also reveals the importance of developing interdisciplinary approaches that extend AI education beyond traditional STEM fields to include humanities and creative subjects, ensuring more comprehensive educational applications.
Limitations: The study acknowledges several limitations including the relatively recent emergence of GAI technologies, which limits long-term impact assessment. The research predominantly focuses on studies from developed countries, potentially limiting global applicability. Additionally, many reviewed studies were theoretical or small-scale implementations rather than large-scale empirical research, which may limit the generalizability of findings. The rapid evolution of AI technologies means some findings may become outdated quickly, and the lack of standardized evaluation metrics across studies makes comparison challenging.
Future Directions: The research identifies eight specific areas requiring future investigation: developing concrete examples of AI in teaching with practical implementation guides; creating comprehensive teacher training programs based on constant AI application; expanding research beyond STEM disciplines to include humanities and creative subjects; conducting more studies in European and other underrepresented geographical contexts; defining clear teacher roles and developing specific pedagogical frameworks like TPACK or AI4K12; exploring AI applications for inclusive education and disability support; connecting GAI with established pedagogical theories and innovative methodologies; and investigating applications of emerging technologies including wearable devices and mobile communication technologies. The study emphasizes the need for longitudinal empirical research to assess long-term impacts and collaborative interdisciplinary approaches involving teachers, students, and technology developers.
Title and Authors: "Generative Artificial Intelligence (GAI) in Teaching and Learning Processes at the K-12 Level: A Systematic Review" by Daniela Marzano.
Published On: April 24, 2025 (Accepted), June 19, 2025 (Published online)
Published By: Technology, Knowledge and Learning (Springer)